Adapting to appearance variations when tracking a target object in video sequence

ABSTRACT

A method of tracking a position of a target object in a video sequence includes identifying the target object in a reference frame. A generic mapping is applied to the target object being tracked. The generic mapping is generated by learning possible appearance variations of a generic object. The method also includes tracking the position of the target object in subsequent frames of the video sequence by determining whether an output of the generic mapping of the target object matches an output of the generic mapping of a candidate object.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/030,685, filed on Jul. 9, 2018, and titled “ADAPTING TOAPPEARANCE VARIATIONS WHEN TRACKING A TARGET OBJECT IN VIDEO SEQUENCE,”which is a continuation of U.S. patent application Ser. No. 15/192,935,filed on Jun. 24, 2016, and titled “ADAPTING TO APPEARANCE VARIATIONSWHEN TRACKING A TARGET OBJECT IN VIDEO SEQUENCE,” which claims thebenefit of U.S. Provisional Patent Application No. 62/251,544, filed onNov. 5, 2015, and titled “GENERIC MAPPING FOR TRACKING TARGET OBJECT INVIDEO SEQUENCE,” the disclosures of which are expressly incorporated byreference herein in their entireties.

BACKGROUND Field

Certain aspects of the present disclosure generally relate to machinelearning and, more particularly, to improving systems and methods oftracking a target object in a video sequence.

Background

An artificial neural network, which may comprise an interconnected groupof artificial neurons (e.g., neuron models), is a computational deviceor represents a method to be performed by a computational device.

Convolutional neural networks are a type of feed-forward artificialneural network. Convolutional neural networks may include collections ofneurons that each has a receptive field and that collectively tile aninput space. Convolutional neural networks (CNNs) have numerousapplications. In particular, CNNs have broadly been used in the area ofpattern recognition and classification.

Deep learning architectures, such as deep belief networks and deepconvolutional networks, are layered neural networks architectures inwhich the output of a first layer of neurons becomes an input to asecond layer of neurons, the output of a second layer of neurons becomesand input to a third layer of neurons, and so on. Deep neural networksmay be trained to recognize a hierarchy of features and so they haveincreasingly been used in object recognition applications. Likeconvolutional neural networks, computation in these deep learningarchitectures may be distributed over a population of processing nodes,which may be configured in one or more computational chains. Thesemulti-layered architectures may be trained one layer at a time and maybe fine-tuned using back propagation.

Other models are also available for object recognition. For example,support vector machines (SVMs) are learning tools that can be appliedfor classification. Support vector machines include a separatinghyperplane (e.g., decision boundary) that categorizes data. Thehyperplane is defined by supervised learning. A desired hyperplaneincreases the margin of the training data. In other words, thehyperplane should have the greatest minimum distance to the trainingexamples.

Although these solutions achieve excellent results on a number ofclassification benchmarks, their computational complexity can beprohibitively high. Additionally, training of the models may bechallenging.

SUMMARY

In an aspect of the present disclosure, a method of tracking a positionof a target object in a video sequence is presented. The method includesidentifying the target object in a reference frame. The method alsoincludes applying a generic mapping to the target object being tracked.The generic mapping is generated by learning possible appearancevariations of a generic object. The method further includes tracking theposition of the target object in subsequent frames of the video sequenceby determining whether an output of the generic mapping of the targetobject matches an output of the generic mapping of a candidate object.

In another aspect of the present disclosure, an apparatus for tracking aposition of a target object in a video sequence is presented. Theapparatus includes a memory and at least one processor coupled to thememory. The one or more processors are configured to identify the targetobject in a reference frame. The processor(s) is(are) also configured toapply a generic mapping to the target object being tracked. The genericmapping is generated by learning possible appearance variations of ageneric object. The processor(s) is(are) further configured to track theposition of the target object in subsequent frames of the video sequenceby determining whether an output of the generic mapping of the targetobject matches an output of the generic mapping of a candidate object.

In yet another aspect of the present disclosure, an apparatus fortracking a position of a target object in a video sequence is presented.The apparatus includes means for identifying the target object in areference frame. The apparatus also includes means for applying ageneric mapping to the target object being tracked. The generic mappingis generated by learning possible appearance variations of a genericobject. The apparatus further includes means for tracking the positionof the target object in subsequent frames of the video sequence bydetermining whether an output of the generic mapping of the targetobject matches an output of the generic mapping of a candidate object.

In still another aspect of the present disclosure, a non-transitorycomputer readable medium is presented. The non-transitory computerreadable medium has encoded thereon program code for tracking a positionof a target object in a video sequence. The program code is executed bya processor and includes program code to identify the target object in areference frame. The program code also includes program code to apply ageneric mapping to the target object being tracked. The generic mappingis generated by learning possible appearance variations of a genericobject. The program code further includes program code to track theposition of the target object in subsequent frames of the video sequenceby determining whether an output of the generic mapping of the targetobject matches an output of the generic mapping of a candidate object.

Additional features and advantages of the disclosure will be describedbelow. It should be appreciated by those skilled in the art that thisdisclosure may be readily utilized as a basis for modifying or designingother structures for carrying out the same purposes of the presentdisclosure. It should also be realized by those skilled in the art thatsuch equivalent constructions do not depart from the teachings of thedisclosure as set forth in the appended claims. The novel features,which are believed to be characteristic of the disclosure, both as toits organization and method of operation, together with further objectsand advantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a neuralnetwork using a system-on-a-chip (SOC), including a general-purposeprocessor in accordance with certain aspects of the present disclosure.

FIG. 2 illustrates an example implementation of a system in accordancewith aspects of the present disclosure.

FIG. 3A is a diagram illustrating a neural network in accordance withaspects of the present disclosure.

FIG. 3B is a block diagram illustrating an exemplary deep convolutionalnetwork (DCN) in accordance with aspects of the present disclosure.

FIG. 4 is a block diagram illustrating an exemplary softwarearchitecture that may modularize artificial intelligence (AI) functionsin accordance with aspects of the present disclosure.

FIG. 5 is a block diagram illustrating the run-time operation of an AIapplication on a smartphone in accordance with aspects of the presentdisclosure.

FIG. 6 is a diagram illustrating an exemplary Siamese network inaccordance with aspects of the present disclosure.

FIG. 7 illustrates a method for feature extraction according to aspectsof the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. However, it will beapparent to those skilled in the art that these concepts may bepracticed without these specific details. In some instances, well-knownstructures and components are shown in block diagram form in order toavoid obscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the disclosure is intended to cover any aspect of thedisclosure, whether implemented independently of or combined with anyother aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth. In addition, the scope of the disclosure is intended to coversuch an apparatus or method practiced using other structure,functionality, or structure and functionality in addition to or otherthan the various aspects of the disclosure set forth. It should beunderstood that any aspect of the disclosure disclosed may be embodiedby one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

An object is an entity in the physical world, which may also refer tothe appearance in an image.

An instance is a specific object, which may also refer to the appearancein the image.

An image patch is a part or portion of an entire image covered by arectangular box (e.g., a bounding box).

In tracking, a target, target object, or target instance, is theinstance selected to track for the video sequence under processing.

An instance search is the task of searching for the same instanceregardless of its appearance variations (e.g., partially occluded).

A mapping function is a function that maps points from one space toanother space. In accordance with aspects of the present disclosure, themapping function takes as input, pixel values of an image and producesas output a representation of the image. The representation may comprisea multi-dimensional vector, which is a point in the correspondingmulti-dimensional space, for instance.

A matching function is a function that compares two images. Matchinginvolves mapping the images to a latent space and then comparing twopoints in the latent space based on a distance computation (e.g.,Euclidean distance).

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to differenttechnologies, system configurations, networks and protocols, some ofwhich are illustrated by way of example in the figures and in thefollowing description of the preferred aspects. The detailed descriptionand drawings are merely illustrative of the disclosure rather thanlimiting, the scope of the disclosure being defined by the appendedclaims and equivalents thereof.

Tracking Target Objects

Tracking models utilize various functions to match a target object in aframe of a video sequence to images of the target in upcoming frames ofthe video sequence. That is, tracking follows an instance. In mosttracking models, the model based on the target object from the firstframe does not account for future appearance changes. Rather, sequentialmodel updating is utilized to adapt to changes in appearance of thetarget object in the upcoming frames of a video sequence.

Conventional tracking models are also negatively impacted by incomingdata and eventually drift. In drifting, gradual false model updatesintroduce parts of the background as if it is the foreground target. Asa consequence, the tracking model may track something different from thetarget.

An online matching function may be utilized to explicitly adapt fordistortions and appearance variations of the target. In particular, eachpossible cause of distortion affecting the target appearance may bemodelled and used by the matching function to adapt for variousdistortions and appearance variations. However, while one mechanism maybe well-fitted for one type of distortion, the mechanism is likely toperform differently with other distortion types. Aspects of the presentdisclosure are directed to teaching a matching mechanism to be invariantto distortions. In particular, a deep convolutional network may learn,offline, a generic mapping based on possible appearance variations fromseparate videos or images and then apply the generic mapping to noveltracking settings.

According to the present disclosure, distortion variances are learnedexternally from separate videos or images, and then applied to noveltracking settings. Starting from external data that contain numeroustypes of variations and do not overlap with the novel tracking videos,the matching function may be improved or even optimized, between anarbitrary target object and candidate patches from subsequent frames.Once the matching function has been learned on the external data, it canbe directly used with all new tracking videos of previously unseentarget objects without further adaptation. Alternatively, if desired,the matching function may be adapted to better fit the target instances.

FIG. 1 illustrates an example implementation of the aforementionedobject tracking using a system-on-a-chip (SOC) 100, which may include ageneral-purpose processor (CPU) or multi-core general-purpose processors(CPUs) 102 in accordance with certain aspects of the present disclosure.Variables (e.g., neural signals and synaptic weights), system parametersassociated with a computational device (e.g., neural network withweights), delays, frequency bin information, and task information may bestored in a memory block associated with a neural processing unit (NPU)108, in a memory block associated with a CPU 102, in a memory blockassociated with a graphics processing unit (GPU) 104, in a memory blockassociated with a digital signal processor (DSP) 106, in a dedicatedmemory block 118, or may be distributed across multiple blocks.Instructions executed at the general-purpose processor 102 may be loadedfrom a program memory associated with the CPU 102 or may be loaded froma dedicated memory block 118.

The SOC 100 may also include additional processing blocks tailored tospecific functions, such as a GPU 104, a DSP 106, a connectivity block110, which may include fourth generation long term evolution (4G LTE)connectivity, unlicensed Wi-Fi connectivity, USB connectivity, Bluetoothconnectivity, and the like, and a multimedia processor 112 that may, forexample, detect and recognize gestures. In one implementation, the NPUis implemented in the CPU, DSP, and/or GPU. The SOC 100 may also includea sensor processor 114, image signal processors (ISPs), and/ornavigation 120, which may include a global positioning system.

The SOC 100 may be based on an ARM instruction set. In an aspect of thepresent disclosure, the instructions loaded into the general-purposeprocessor 102 may comprise code for identifying the target object in areference frame. The instructions loaded into the general-purposeprocessor 102 may also comprise code for applying a generic mapping tothe target object being tracked. The generic mapping is generated bylearning possible appearance variations of a generic object. Theinstructions loaded into the general-purpose processor 102 may furthercomprise code for tracking a position of the target object in subsequentframes of the video sequence by determining whether an output of thegeneric mapping of the target object matches an output of the genericmapping of a candidate object.

FIG. 2 illustrates an example implementation of a system 200 inaccordance with certain aspects of the present disclosure. Asillustrated in FIG. 2, the system 200 may have multiple local processingunits 202 that may perform various operations of methods describedherein. Each local processing unit 202 may comprise a local state memory204 and a local parameter memory 206 that may store parameters of aneural network. In addition, the local processing unit 202 may have alocal (neuron) model program (LMP) memory 208 for storing a local modelprogram, a local learning program (LLP) memory 210 for storing a locallearning program, and a local connection memory 212. Furthermore, asillustrated in FIG. 2, each local processing unit 202 may interface witha configuration processor unit 214 for providing configurations forlocal memories of the local processing unit, and with a routingconnection processing unit 216 that provides routing between the localprocessing units 202.

Deep learning architectures may perform an object recognition task bylearning to represent inputs at successively higher levels ofabstraction in each layer, thereby building up a useful featurerepresentation of the input data. In this way, deep learning addresses amajor bottleneck of traditional machine learning. Prior to the advent ofdeep learning, a machine learning approach to an object recognitionproblem may have relied heavily on human engineered features, perhaps incombination with a shallow classifier. A shallow classifier may be atwo-class linear classifier, for example, in which a weighted sum of thefeature vector components may be compared with a threshold to predict towhich class the input belongs. Human engineered features may betemplates or kernels tailored to a specific problem domain by engineerswith domain expertise. Deep learning architectures, in contrast, maylearn to represent features that are similar to what a human engineermight design, but through training. Furthermore, a deep network maylearn to represent and recognize new types of features that a humanmight not have considered.

A deep learning architecture may learn a hierarchy of features. Ifpresented with visual data, for example, the first layer may learn torecognize relatively simple features, such as edges, in the inputstream. In another example, if presented with auditory data, the firstlayer may learn to recognize spectral power in specific frequencies. Thesecond layer, taking the output of the first layer as input, may learnto recognize combinations of features, such as simple shapes for visualdata or combinations of sounds for auditory data. For instance, higherlayers may learn to represent complex shapes in visual data or words inauditory data. Still higher layers may learn to recognize common visualobjects or spoken phrases.

Deep learning architectures may perform especially well when applied toproblems that have a natural hierarchical structure. For example, theclassification of motorized vehicles may benefit from first learning torecognize wheels, windshields, and other features. These features may becombined at higher layers in different ways to recognize cars, trucks,and airplanes.

Neural networks may be designed with a variety of connectivity patterns.In feed-forward networks, information is passed from lower to higherlayers, with each neuron in a given layer communicating to neurons inhigher layers. A hierarchical representation may be built up insuccessive layers of a feed-forward network, as described above. Neuralnetworks may also have recurrent or feedback (also called top-down)connections. In a recurrent connection, the output from a neuron in agiven layer may be communicated to another neuron in the same layer. Arecurrent architecture may be helpful in recognizing patterns that spanmore than one of the input data chunks that are delivered to the neuralnetwork in a sequence. A connection from a neuron in a given layer to aneuron in a lower layer is called a feedback (or top-down) connection. Anetwork with many feedback connections may be helpful when therecognition of a high level concept may aid in discriminating theparticular low level features of an input.

Referring to FIG. 3A, the connections between layers of a neural networkmay be fully connected 302 or locally connected 304. In a fullyconnected network 302, a neuron in a first layer may communicate itsoutput to every neuron in a second layer, so that each neuron in thesecond layer will receive input from every neuron in the first layer.Alternatively, in a locally connected network 304, a neuron in a firstlayer may be connected to a limited number of neurons in the secondlayer. A convolutional network 306 may be locally connected, and isfurther configured such that the connection strengths associated withthe inputs for each neuron in the second layer are shared (e.g., 308).More generally, a locally connected layer of a network may be configuredso that each neuron in a layer will have the same or a similarconnectivity pattern, but with connections strengths that may havedifferent values (e.g., 310, 312, 314, and 316). The locally connectedconnectivity pattern may give rise to spatially distinct receptivefields in a higher layer, because the higher layer neurons in a givenregion may receive inputs that are tuned through training to theproperties of a restricted portion of the total input to the network.

Locally connected neural networks may be well suited to problems inwhich the spatial location of inputs is meaningful. For instance, anetwork 300 designed to recognize visual features from a car-mountedcamera may develop high layer neurons with different propertiesdepending on their association with the lower versus the upper portionof the image. Neurons associated with the lower portion of the image maylearn to recognize lane markings, for example, while neurons associatedwith the upper portion of the image may learn to recognize trafficlights, traffic signs, and the like.

A deep convolutional network (DCN) may be trained with supervisedlearning. During training, a DCN may be presented with an image, such asa cropped image of a speed limit sign 326, and a “forward pass” may thenbe computed to produce an output 322. The output 322 may be a vector ofvalues corresponding to features such as “sign,” “60,” and “100.” Thenetwork designer may want the DCN to output a high score for some of theneurons in the output feature vector, for example the ones correspondingto “sign” and “60” as shown in the output 322 for a network 300 that hasbeen trained. Before training, the output produced by the DCN is likelyto be incorrect, and so an error may be calculated between the actualoutput and the target output. The weights of the DCN may then beadjusted so that the output scores of the DCN are more closely alignedwith the target.

To adjust the weights, a learning algorithm may compute a gradientvector for the weights. The gradient may indicate an amount that anerror would increase or decrease if the weight were adjusted slightly.At the top layer, the gradient may correspond directly to the value of aweight connecting an activated neuron in the penultimate layer and aneuron in the output layer. In lower layers, the gradient may depend onthe value of the weights and on the computed error gradients of thehigher layers. The weights may then be adjusted so as to reduce theerror. This manner of adjusting the weights may be referred to as “backpropagation” as it involves a “backward pass” through the neuralnetwork.

In practice, the error gradient of weights may be calculated over asmall number of examples, so that the calculated gradient approximatesthe true error gradient. This approximation method may be referred to asstochastic gradient descent. Stochastic gradient descent may be repeateduntil the achievable error rate of the entire system has stoppeddecreasing or until the error rate has reached a target level.

After learning, the DCN may be presented with new images 326 and aforward pass through the network may yield an output 322 that may beconsidered an inference or a prediction of the DCN.

Deep belief networks (DBNs) are probabilistic models comprising multiplelayers of hidden nodes. DBNs may be used to extract a hierarchicalrepresentation of training data sets. A DBN may be obtained by stackingup layers of Restricted Boltzmann Machines (RBMs). An RBM is a type ofartificial neural network that can learn a probability distribution overa set of inputs. Because RBMs can learn a probability distribution inthe absence of information about the class to which each input should becategorized, RBMs are often used in unsupervised learning. Using ahybrid unsupervised and supervised paradigm, the bottom RBMs of a DBNmay be trained in an unsupervised manner and may serve as featureextractors, and the top RBM may be trained in a supervised manner (on ajoint distribution of inputs from the previous layer and target classes)and may serve as a classifier.

Deep convolutional networks (DCNs) are networks of convolutionalnetworks, configured with additional pooling and normalization layers.DCNs have achieved state-of-the-art performance on many tasks. DCNs canbe trained using supervised learning in which both the input and outputtargets are known for many exemplars and are used to modify the weightsof the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, theconnections from a neuron in a first layer of a DCN to a group ofneurons in the next higher layer are shared across the neurons in thefirst layer. The feed-forward and shared connections of DCNs may beexploited for fast processing. The computational burden of a DCN may bemuch less, for example, than that of a similarly sized neural networkthat comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may beconsidered a spatially invariant template or basis projection. If theinput is first decomposed into multiple channels, such as the red,green, and blue channels of a color image, then the convolutionalnetwork trained on that input may be considered three-dimensional, withtwo spatial dimensions along the axes of the image and a third dimensioncapturing color information. The outputs of the convolutionalconnections may be considered to form a feature map in the subsequentlayer 318 and 320, with each element of the feature map (e.g., 320)receiving input from a range of neurons in the previous layer (e.g.,318) and from each of the multiple channels. The values in the featuremap may be further processed with a non-linearity, such as arectification, max(0,x). Values from adjacent neurons may be furtherpooled, which corresponds to down sampling, and may provide additionallocal invariance and dimensionality reduction. Normalization, whichcorresponds to whitening, may also be applied through lateral inhibitionbetween neurons in the feature map.

The performance of deep learning architectures may increase as morelabeled data points become available or as computational powerincreases. Modern deep neural networks are routinely trained withcomputing resources that are thousands of times greater than what wasavailable to a typical researcher just fifteen years ago. Newarchitectures and training paradigms may further boost the performanceof deep learning. Rectified linear units may reduce a training issueknown as vanishing gradients. New training techniques may reduceover-fitting and thus enable larger models to achieve bettergeneralization. Encapsulation techniques may abstract data in a givenreceptive field and further boost overall performance.

FIG. 3B is a block diagram illustrating an exemplary deep convolutionalnetwork 350. The deep convolutional network 350 may include multipledifferent types of layers based on connectivity and weight sharing. Asshown in FIG. 3B, the exemplary deep convolutional network 350 includesmultiple convolution blocks (e.g., C1 and C2). Each of the convolutionblocks may be configured with a convolution layer, a normalization layer(LNorm), and a pooling layer. The convolution layers may include one ormore convolutional filters, which may be applied to the input data togenerate a feature map. Although only two convolution blocks are shown,the present disclosure is not so limiting, and instead, any number ofconvolutional blocks may be included in the deep convolutional network350 according to design preference. The normalization layer may be usedto normalize the output of the convolution filters. For example, thenormalization layer may provide whitening or lateral inhibition. Thepooling layer may provide down sampling aggregation over space for localinvariance and dimensionality reduction.

The parallel filter banks, for example, of a deep convolutional networkmay be loaded on a CPU 102 or GPU 104 of an SOC 100, optionally based onan ARM instruction set, to achieve high performance and low powerconsumption. In alternative embodiments, the parallel filter banks maybe loaded on the DSP 106 or an ISP 116 of an SOC 100. In addition, theDCN may access other processing blocks that may be present on the SOC,such as processing blocks dedicated to sensors 114 and navigation 120.

The deep convolutional network 350 may also include one or more fullyconnected layers (e.g., FC1 and FC2). The deep convolutional network 350may further include a logistic regression (LR) layer. Between each layerof the deep convolutional network 350 are weights (not shown) that areto be updated. The output of each layer may serve as an input of asucceeding layer in the deep convolutional network 350 to learnhierarchical feature representations from input data (e.g., images,audio, video, sensor data and/or other input data) supplied at the firstconvolution block C1.

FIG. 4 is a block diagram illustrating an exemplary softwarearchitecture 400 that may modularize artificial intelligence (AI)functions. Using the architecture, applications 402 may be designed thatmay cause various processing blocks of an SOC 420 (for example a CPU422, a DSP 424, a GPU 426 and/or an NPU 428) to perform supportingcomputations during run-time operation of the application 402.

The AI application 402 may be configured to call functions defined in auser space 404 that may, for example, provide for the detection andrecognition of a scene indicative of the location in which the devicecurrently operates. The AI application 402 may, for example, configure amicrophone and a camera differently depending on whether the recognizedscene is an office, a lecture hall, a restaurant, or an outdoor settingsuch as a lake. The AI application 402 may make a request to compiledprogram code associated with a library defined in a SceneDetectapplication programming interface (API) 406 to provide an estimate ofthe current scene. This request may ultimately rely on the output of adeep neural network configured to provide scene estimates based on videoand positioning data, for example.

A run-time engine 408, which may be compiled code of a RuntimeFramework, may be further accessible to the AI application 402. The AIapplication 402 may cause the run-time engine, for example, to request ascene estimate at a particular time interval or triggered by an eventdetected by the user interface of the application. When caused toestimate the scene, the run-time engine may in turn send a signal to anoperating system 410, such as a Linux Kernel 412, running on the SOC420. The operating system 410, in turn, may cause a computation to beperformed on the CPU 422, the DSP 424, the GPU 426, the NPU 428, or somecombination thereof. The CPU 422 may be accessed directly by theoperating system, and other processing blocks may be accessed through adriver, such as a driver 414-418 for a DSP 424, for a GPU 426, or for anNPU 428. In the exemplary example, the deep neural network may beconfigured to run on a combination of processing blocks, such as a CPU422 and a GPU 426, or may be run on an NPU 428, if present.

FIG. 5 is a block diagram illustrating the run-time operation 500 of anAI application on a smartphone 502. The AI application may include apre-process module 504 that may be configured (using for example, theJAVA programming language) to convert the format of an image 506 andthen crop and/or resize the image 508. The pre-processed image may thenbe communicated to a classify application 510 that contains aSceneDetect Backend Engine 512 that may be configured (using forexample, the C programming language) to detect and classify scenes basedon visual input. The SceneDetect Backend Engine 512 may be configured tofurther preprocess 514 the image by scaling 516 and cropping 518. Forexample, the image may be scaled and cropped so that the resulting imageis 224 pixels by 224 pixels. These dimensions may map to the inputdimensions of a neural network. The neural network may be configured bya deep neural network block 520 to cause various processing blocks ofthe SOC 100 to further process the image pixels with a deep neuralnetwork. The results of the deep neural network may then be thresholded522 and passed through an exponential smoothing block 524 in theclassify application 510. The smoothed results may then cause a changeof the settings and/or the display of the smartphone 502.

According to certain aspects of the present disclosure, each localprocessing unit 202 may be configured to determine parameters of themodel based upon desired one or more functional features of the model,and develop the one or more functional features towards the desiredfunctional features as the determined parameters are further adapted,tuned and updated.

Generic Mapping for Tracking

Tracking models utilize various functions to match a target object in aframe of a video sequence to images of the target in upcoming frames ofthe video sequence. For example, the most basic concept of trackingincludes the direct matching between the intensity values of pixels ofthe initial target box and patches taken from the incoming image. Amatching-based tracking model may focus on various distortionsencountered in tracking. In particular, the target object may appeardifferently in upcoming frames as compared to a reference frame of thetarget (which may be the first frame). For example, the target objectmay become occluded, change in scale, and/or rotate in and out-of-plane.Additionally, changes in illumination and camera angle may also impactthe appearance of the target object. A matching function may be utilizedto track and match the target object in upcoming frames of the videosequence in view of possible distortion and appearance variations.

In most tracking models, the initial model taken from the first framedoes not account for future appearance changes. Rather, sequential modelupdating is utilized to adapt to changes in the appearance of the targetobject in the upcoming frames of a video sequence. Additionally, a modelof each and every possible cause of distortion affecting the targetappearance may be utilized by a matching mechanism to explicitly adaptfor distortions and appearance variations of the target. However, whileone mechanism is well-fitted for one type of distortion, the mechanismis likely to perform differently with other distortion types.

Aspects of the present disclosure are directed to utilizing a genericmapping mechanism for tracking a target object in a video sequence. Inparticular, the generic mapping is generated based on offline learningof appearance variations, such as, but not limited to scale changes,illumination variations, etc. The appearance variations may be learnedfrom offline videos. For example, the appearance variations may belearned from a set of videos in a video repository (e.g., 300 videosequences). The offline learning may be used to learn how the appearanceof an object (e.g., a moving object) may transition from one frame tothe next in a video sequence. In one aspect, the training videos includeexternal data containing many types of appearance variations and do notoverlap with novel tracking videos. That is, the learned appearancevariations are based on generic objects rather than the specific object(same object). The training videos may not even include examples of thesame class of objects as the class to which the target object belongs.For instance, the learned appearance variations of a car may be appliedto determine how a truck may appear from one frame to the next in avideo sequence. Once the matching function has been trained on theexternal data, the mapping function may be used with new tracking videosof previously unseen target objects.

In another aspect, the matching function may be trained to learn onlytypical tracking variations (based on typical appearance variations anddistortion). The resulting generic mapping function is not afull-fledged tracker, with highly intelligent model update mechanisms,etc. For example, in one aspect, at each frame in a video sequence, thecandidate patches are compared with a target object from a referenceframe, without relying on the predictions of the previous frames formodel updating.

Aspects of the present disclosure are directed to a method of tracking atarget object in a video sequence, where a machine learning model istrained to learn matching functions on external, separate data (e.g.,offline), instead of manually modelling the matching variationson-the-fly. The training is performed offline on completely separatedata. In some aspects, the target object, similar objects or similarcategories of object may be absent from the external data. That is,because the mapping function learns generic appearance variations of anobject, target objects may be tracked even where the external data doesnot include the same object, similar objects, or even objects in thesame or a similar category as the tracking videos, for use later.Accordingly, the resulting generic mapping is applied to new trackingvideos and new target objects, regardless of the object appearance orclass type.

In another aspect, a neural network having a Siamese architecture isutilized to model and train the generic mapping. The customized networkis trained to recognize whether two image patches depict the sameobject. Optionally, the customized network may be configured to performmatching from different object viewpoints (e.g., from the frontal viewof the car to the side view of the car).

In one aspect, the machine learning model is trained to learn trackinginvariances. In one example, two sets of data are assumed: X={X^(i)},i=1, . . . , D and Z={Z^(i)}, i=1, . . . , D, such that X^(i)={x_(j)^(i)}, j=1, . . . , T are frames of a particular object in a video.Because in the learning set the ground truth locations of an instance ina video are known, the identity of the object can be certain regardlessof the variations in the viewing conditions (e.g., scale changes,illumination differences, deformations, etc.) Given the availabletraining data, the model f(⋅; θ) with parameters θ, may be improved orin some cases optimized, for example, according to the following:

min L(f(x _(j) ^(i);θ),f(x _(k) ^(i);θ),y _(jk))∀j,k  (1)

where y_(jk)∈{0, 1} is a label indicating whether x_(j) and x_(k) referto the same instance, whereas L(⋅) is a loss function measuring how wellf(⋅; θ) can map different images of the same instance to a similar partof the space. An instance is defined as a specific object, and may alsorefer to the appearance thereof in an image.

Optimizing the loss L on the dataset X alone may result ingeneralization issues, because the extent to which a representation isinvariant or simply over-fit is not known. The parameters may beoptimized using X, while Z serves as a validation set to monitor thegeneralization capacity of the learned model.

A second separate dataset Z is utilized to reliably learn the trackinginvariances. To determine the final invariance model, the performance ofthe invariance model in both datasets X, Z is improved or optimized.Those skilled in the art will appreciate a variety of methods may beemployed to perform the optimization. For illustrative purposes only,the following example is provided where X is the training set where θ isimproved or even optimized, while Z is the validation set where thegeneralization capacity of the network is monitored.

To learn invariances, a model that operates on pairs of image patches,(x_(j), x_(k)) is utilized. In some aspects, a customized Siamesenetwork may be utilized to operate on the image patch pairs. Siamesenetworks generally include two identical subnetworks that are joined atthe outputs. The customized Siamese network may be constructed on top ofa convolutional neural network such as DCN 350, for example.

FIG. 6 is a diagram illustrating an exemplary modified Siamese network600 in accordance with aspects of the present disclosure. Referring toFIG. 6, the network 600 includes two branches (e.g., Query Stream andSearch Stream). Each branch of the network 600 may take the form of aconvolutional neural network. In some aspects, the two branches of thenetwork 600 of FIG. 6 may have the same structure and parameter valuessuch that they perform the same operation. Of course, this is merelyexemplary and the two branches may also be differentially configured.Each branch may receive and process a separate input. In turn, theindividual branches of the network 600 generate a generic mapping usedfor tracking unknown target objects. The individual branches of thenetwork 600 (e.g., Query Stream and Search Stream) may operate as amapping function, while the network 600 may operate as the matchingfunction. In the layer connecting the two branches, there is a distancecomparison.

As shown in FIG. 6, each branch of the network may be configured withone or more convolutional layers (e.g., cony 1, cony 2, cony 3, cony 4),max pooling layers (e.g., maxpool 1, maxpool 2), and/or region poolinglayers (e.g., region of interest pooling shown as roi pool). The numberof and types of layers is exemplary and non-limiting. The network 600may also include a loss layer (e.g., margin contrastive loss).

The max pooling layers maintain only the strongest of the activations(e.g., highest activation value) from a local neighborhood to use asinput for the subsequent layers. As such, the spatial resolution of theactivation inputs is aggressively reduced (e.g., by 50% in the simplecase of 2×2 of local neighborhoods). In some aspects, the max poolinglayers may be selectively removed so that pixel level accuracy may beconsidered. In one exemplary aspect, max pooling layers of a standardconvolutional network that maintain invariance to noise may be retainedand the other max pooling layers may be removed.

In tracking, several hundreds of candidate locations may be evaluatedfor the next frame. Parsing through the candidate locationsindependently may result in severe computational overhead. To overcomethe computational overhead, the modified Siamese network employs aregion pooling layer (e.g., roi pooling) for the fast processing ofmultiple overlapping regions.

Each branch of the network 600 receives, as input, one entire frame(e.g., 602, 606) and a set of patches specified by rectangles (e.g.,604, 608). In the example of FIG. 6, two images are supplied as inputs(602, 606). Each of the inputs depicts an animal of the Felidae family(e.g., lion or leopard) from a different perspective (e.g., side orrear). The network 600 may, for example, process the entire image for afew layers. Next, the region pooling layer converts the feature map froma particular region into a fixed-length representation, and next thesubsequent layers are processed.

Notably, the layers in a deep network may progressively capturerepresentations that are more abstract. The filters of the lower layersmay be activated by lower level visual patterns, such as edges andangles. Higher layers are generally activated by more complex patterns,such as faces and wheels. Additionally, the deeper the layer, the moreinvariant the layer is to appearance variations and also the lessdiscriminative it is, especially for instance-level distinction.

In tracking, the type of the target object being tracked is generallynot known. In other words, it is not known whether the target object ishighly textured with low level patterns or not. Accordingly, in oneaspect, the outputs from multiple layers are used as the intermediatefeature representation that is then fed to a loss function. That is,multiple layers are used for feature extraction. All activations can belocally pooled using the region pooling layers.

Conventional convolutional neural networks use rectified linear unitsthat do not bound the output values and the nonlinear activations mayvary in the range of values produced. Accordingly, the network outputand the loss function are heavily influenced by the scale of thegenerated features and not by their representation quality.

In contrast, in some aspects of the present disclosure, an L2normalization layer (shown as normalization) may be included in eachbranch before the loss layer. In one example, the normalization layermay be applied on each of the layer activations that are fed to the losslayer and includes the property of maintaining the direction of thefeature, while forcing features from different scales to lie on the sameunit sphere (e.g., a set of points one unit from a fixed central point).The parameters of the two convolutional network branches may be tiedtogether (e.g., two branches have identical network structure andparameter values), reducing the danger of overfitting.

The training data includes videos of objects, with known bounding box(or box) locations. In one aspect, the first stream of the network 600shown in FIG. 6 is the query stream and the second stream is the searchstream. For the query stream, one frame from the video is randomlyselected. The original box covering the target object available from theground truth is also used for the query stream. Another video frame israndomly selected for the search stream. Boxes are sampled from theframe of the search stream. The boxes that overlap more than a thresholdvalue ρ+ with the ground truth may be deemed positives. The boxes thatoverlap less than threshold value ρ− with the ground truth may be deemednegatives. As such, these values may be used to form positive andnegative pairs of image patches that may be used for the training.

The two branches of the network 600 are connected with a single losslayer. For tracking, one goal is to obtain a good localization, or inother words, to detect the general location of the target object in aframe (e.g., the new frame). At the same time, it may be desirable toencourage the network to generate representations that are close in thespace for image pairs of the same instance while being far away forpairs of different objects. Accordingly, the following margincontrastive loss may be employed:

(x _(j) ,x _(k) ,y _(jk))=½y _(jk) D ²+½(1−y _(jk))max(0,∈−D ²)  (2)

where D=∥f(x_(j))−f(x_(k))∥ is the Euclidean distance of two L2normalized representations, y_(jk)∈{0, 1} indicates whether x_(j) andx_(k) are the same object, and E is the minimum distance margin that twoimages of different objects may satisfy. Although, D is expressed as aEuclidean distance, this is merely exemplary and other distance measuresmay also be employed.

In operation, each branch of network 600 may takes pixel values of animage patch as input and produces a multi-dimensional vector as therepresentation of the image. As such, the branches of network 600 mayoperate as a generic mapping function that maps the pixel values of theimage into a representation. The loss (e.g., margin contrastive loss)connecting the two branches (e.g., Query Stream and Search Stream) maybe used to guide the learning such that the mapping may generate similarrepresentations for two images (e.g., 602, 606) of the same object whilegenerating different enough (e.g., distinguishable) representations fortwo images showing different objects.

In one example, several hundreds of candidate boxes (e.g., candidateimage patches) may be evaluated to find the location of a target (e.g.,leopard) in the next frame. In conventional approaches, the imagepatches may be supplied independently, one by one, to a branch network.In this way, the conventional branch network only takes one input thatis the image patch, fed into a convolution layer. However, this may leadto severe computation overhead, especially because there may besignificant overlap between the candidate patches.

The network 600 of FIG. 6 illustrates a more efficient approach, usingthe region-of-interest pooling layers (roi pool). With theregion-of-interest pooling layer, the network 600 takes two inputs(e.g., 602 and 604 or 606 and 608). One input (e.g., 602,606) is theentire frame fed into convolutional layer (convl), and the other inputis the coordinates of the candidate boxes (e.g., 604, 608) fed into theregion-of-interest pooling layer (e.g., 604, 608). Each of the branchesof network 600 begins processing the entire frame for a few steps, thenthe region-of-interesting pooling layer converts a feature map coveredby a candidate box into a fixed-length representation for furtherprocessing. In this way, the network can process hundreds of candidatepatches in one single pass through the network.

In another aspect, the application of the generic mapping does notresult in model drift and allows for target object recovery:

In aspects of the present disclosure, all invariances are learned onexternal data and videos that do not appear in the tracking dataset. Thedata includes enough variations to cover various semantics and do notfocus on particular objects. Additionally, the general mapping is nottrained to learn explicit types of invariances. In particular,“illumination invariance” is not learned separately from “scalein-variance.” Accordingly, the external data does not need any specificinvariance labels. The bounding box annotations within the video shouldfollow a particular object. The video data should also contain a goodamount of variations where the model can learn the invariances desiredfor the final tracker.

Once the network had been trained to learn invariances offline in thelearning phase, the resulting general mapping may be applied to onlinetracking. One source of reliable data for the target object is itslocation at the reference frame. Thus, at each frame, the sampledcandidate boxes may be compared with the target object at the referenceframe. Of course, this is merely exemplary and the sampled candidateboxes may also be compared with a picture of the target object (not inthe video sequence) or other source of reliable data. Next, thecandidate boxes are passed from the search stream of the customizednetwork and the candidate box most similar to the original target isselected:

$\begin{matrix}{{{\hat{x}}_{t} = {\underset{x_{j,t}}{\arg \; \min}\; {D\left( {x_{t = 0},x_{j,t}} \right)}}},} & (3)\end{matrix}$

where x_(j,t) are all the candidate boxes at frames t. Althoughdescribed with respect to a particular tracking technique, the matchingfunctions learned on external data can also be combined with moresophisticated tracker inference models, which include update and forgetmechanisms.

Various techniques may be employed to sample candidate locations. In oneaspect, candidate samples are taken around the predicted location of theprevious frame. For example, K boxes may be sampled evenly on concentriccircles of different radii. Additionally, multiple candidate boxes atdifferent scales may be generated to address scale variations.

A refinement step may be added to improve localization accuracy of theboxes. For example, the predicted bounding box may be refined at eachframe using a type of regression, such as ridge regression. The ridgeregressors can be trained for the (x, y) coordinates of the box centerand the width and height (w, h) of the box based on the first frame. Theregressors are not updated during tracking in order to avoid the risk ofcontaminating the regressors with noisy data. For each frame, theregressors take the representation of the picked candidate box as inputand produce a refined box.

In one configuration, a machine learning model identifies the targetobject in a reference frame. The model also applies a generic mapping tothe target object being tracked. The model further tracks the positionof the target object in subsequent frames of the video sequence bydetermining whether an output of the generic mapping of the targetobject matches an output of the generic mapping of a candidate object.The model includes identifying means, applying means, and/or trackingmeans. In one aspect, the identifying means, applying means, and/ortracking means may be the general-purpose processor 102, program memoryassociated with the general-purpose processor 102, memory block 118,local processing units 202, and or the routing connection processingunits 216 configured to perform the functions recited. In anotherconfiguration, the aforementioned means may be any module or anyapparatus configured to perform the functions recited by theaforementioned means.

FIG. 7 illustrates a method 700 for tracking a target object in a videosequence. In block 702, the process identifies the target object in areference frame.

In some aspects, the process may optionally include training to learnpossible appearance variations of an object, in block 704. Theappearance variations may be learned from pairs of annotated digitalimages. The appearance variations may be relative to a same instances(e.g., same object or class of object) or in some aspects, may berelative to a pair of different objects (e.g., objects from differentclasses). In one example, the appearance variations may be learned via aneural network having a Siamese architecture for use with tracking theposition of the target object in the video sequence.

Additionally, in some aspects, the process may optionally learn ageneric mapping to cope with possible appearance variations of a genericobject, in block 706. The generic mapping may be learned from an offlinetraining process, and in some aspects, exclusively from an offlinelearning process. The training process may, for example, include offlineanalyzing of annotated video of objects, which may or may not includethe target object.

In block 708, the process applies the generic mapping to a targetobject. In block 710, the process tracks a position of the target objectin subsequent frames of the video sequence by determining whether anoutput of the generic mapping of the target object matches an output ofthe generic mapping of a candidate object.

In some aspects, the method 700 may be performed by the SOC 100 (FIG. 1)or the system 200 (FIG. 2). That is, each of the elements of method 700may, for example, but without limitation, be performed by the SOC 100 orthe system 200 or one or more processors (e.g., CPU 102 and localprocessing unit 202) and/or other components included therein.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general-purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and generalprocessing, including the execution of software stored on themachine-readable media. The processor may be implemented with one ormore general-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Software shall be construed broadly to meaninstructions, data, or any combination thereof, whether referred to assoftware, firmware, middleware, microcode, hardware descriptionlanguage, or otherwise. Machine-readable media may include, by way ofexample, random access memory (RAM), flash memory, read only memory(ROM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable programmable Read-onlymemory (EEPROM), registers, magnetic disks, optical disks, hard drives,or any other suitable storage medium, or any combination thereof. Themachine-readable media may be embodied in a computer-program product.The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or general register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured as a general-purpose processingsystem with one or more microprocessors providing the processorfunctionality and external memory providing at least a portion of themachine-readable media, all linked together with other supportingcircuitry through an external bus architecture. Alternatively, theprocessing system may comprise one or more neuromorphic processors forimplementing the neuron models and models of neural systems describedherein. As another alternative, the processing system may be implementedwith an application specific integrated circuit (ASIC) with theprocessor, the bus interface, the user interface, supporting circuitry,and at least a portion of the machine-readable media integrated into asingle chip, or with one or more field programmable gate arrays (FPGAs),programmable logic devices (PLDs), controllers, state machines, gatedlogic, discrete hardware components, or any other suitable circuitry, orany combination of circuits that can perform the various functionalitydescribed throughout this disclosure. Those skilled in the art willrecognize how best to implement the described functionality for theprocessing system depending on the particular application and theoverall design constraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a generalregister file for execution by the processor. When referring to thefunctionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Additionally, anyconnection is properly termed a computer-readable medium. For example,if the software is transmitted from a website, server, or other remotesource using a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects computer-readable media maycomprise non-transitory computer-readable media (e.g., tangible media).In addition, for other aspects computer-readable media may comprisetransitory computer-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of computer-readablemedia.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A device for tracking a target object in asequence of images captured by a vehicle-mounted camera, comprising: amemory; and a processor, coupled to the memory, configured to: obtain ageneric mapping of an object, wherein the generic mapping is based onappearance variations of the object; obtain an image of the targetobject from the sequence of images captured by the vehicle-mountedcamera; and identify the target object in a subsequent image bydetermining that the features of the generic mapping of the object matchfeatures of the target object in the subsequent image.
 2. The device ofclaim 1, wherein the processor is further configured to: obtain an imageof the object in a sequence of reference frames; and generate thegeneric mapping of the object by learning appearance variations of theobject.
 3. The device of claim 2, wherein learning appearance variationsof the object comprises offline learning based on videos or images in arepository.
 4. The device of claim 1, wherein the object corresponds toone or more of a vehicle wheel, a vehicle windshield, a traffic light,or a traffic sign.
 5. The device of claim 1, wherein determining whetherfeatures of the generic mapping of the object match features of thetarget object in the subsequent image comprises determining whetherfeatures of the generic mapping of the object match features of acandidate box of a plurality of candidate boxes of the target object. 6.The device of claim 1, wherein the appearance variations are relative tothe same instances of the object or are relative to a pair of differentobjects from different classes.
 7. The device of claim 6, wherein thegeneric mapping represents a frontal view and a side view of a genericcar.
 8. The device of claim 6, wherein the generic mapping is generatedbased on reference images that do not include a truck.
 9. The device ofclaim 1, wherein a type of the target object is not determined.
 10. Thedevice of claim 1, wherein the target object corresponds to an airplane.11. The device of claim 1, wherein the device comprises a vehicle, thevehicle comprising the vehicle-mounted camera coupled to the processor.12. A method of tracking a target object in a sequence of imagescaptured by a vehicle-mounted camera, comprising: obtaining a genericmapping of an object, wherein the generic mapping based on appearancevariations of the object; obtaining an image of the target object fromthe sequence of images captured by the vehicle-mounted camera; andidentifying the target object in a subsequent image by determining thatthe features of the generic mapping of the generic object match featuresof the target object in the subsequent image.
 13. The method of claim12, further comprising: obtaining an image of the object in a sequenceof reference frames; and generating the generic mapping of the object bylearning appearance variations of the object.
 14. The method of claim13, wherein learning appearance variations of the object comprisesoffline learning based on videos or images in a repository.
 15. Themethod of claim 12, wherein the object corresponds to one or more of avehicle wheel, a vehicle windshield, a traffic light, or a traffic sign16. The method of claim 12, wherein determining whether features of thegeneric mapping of the object match features of the target object in thesubsequent image comprises determining whether features of the genericmapping of the object match features of a candidate box of a pluralityof candidate boxes of the target object.
 17. The method of claim 12,wherein appearance variations are relative to the same instances of theobject or are relative to a pair of different objects from differentclasses.
 18. The method of claim 17, wherein the generic mappingrepresents a frontal view and a side view of a generic car.
 19. Themethod of claim 17, wherein the generic mapping is generated based onreference images that do not include a truck.
 20. The method of claim12, wherein a type of the target object is not determined.
 21. Themethod of claim 12, wherein the target object corresponds to anairplane.
 22. A non-transitory computer-readable medium for tracking atarget object in a sequence of images captured by a vehicle-mountedcamera, the non-transitory computer-readable medium storing instructionsthat, when executed by a processor of a device, cause the device toperform a method comprising: obtaining a generic mapping of an object,wherein the generic mapping based on appearance variations of theobject; obtaining an image of the target object from the sequence ofimages captured by the vehicle-mounted camera; and identifying thetarget object in a subsequent image by determining that the features ofthe generic mapping of the generic object match features of the targetobject in the subsequent image.